They all rely on the same generic PyanNet architecture which is depicted in Figure REF . Sequence labeling tasks were trained on 2s audio chunks, either with handcrafted MFCC features (19 coefficients extracted every 10ms on 25ms windows, with first- and second-order derivatives) or with trainable SincNet features (using the configuration of the original paper [1]}). The end-to-end variant consistently outperforms the one based on handcrafted features for all tasks but speaker embedding (for which we have yet to train the handcrafted features variant), defining a new state-of-the-art performance for most cases.
